Describing Data

Session 1

Luis F. Gómez L.

luis.gomezl@unimilitar.edu.co

Distance Learning Faculty

15 September, 2025

Please Read Me

This presentation is based on (Chapman & Feit, 2019, Chapter 3)

Purpose
- Utilize descriptive statistics and single variable visualization techniques for summarizing and exploring a data set

Weekly store data

storeNum: store identifier
Year: year identifier
Week: week as it would appear in the ISO 8601 system (1-52)
p1sales: units sold of product 1
p2sales: units sold of product 2
p1price: price of product 1
p2price: price of product 2
p1prom: whether product 1 was promoted (1) or not (0)
p2prom: whether product 2 was promoted (1) or not (0)
country: two-letter country codes defined in ISO 3166-1

Import data

weekly_store <- read_csv(file = "http://goo.gl/QPDdMl")
weekly_store |> head(n=5)

# A tibble: 5 × 10
  storeNum  Year  Week p1sales p2sales p1price p2price p1prom p2prom country
     <dbl> <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl> <chr>  
1      101     1     1     127     106    2.29    2.29      0      0 US     
2      101     1     2     137     105    2.49    2.49      0      0 US     
3      101     1     3     156      97    2.99    2.99      1      0 US     
4      101     1     4     117     106    2.99    3.19      0      0 US     
5      101     1     5     138     100    2.49    2.59      0      1 US

Transform data

weekly_store <- weekly_store |>
  mutate(storeNum = factor(storeNum, ordered = FALSE),
         Year = factor(Year, levels = 1:2, ordered = TRUE),
         Week = factor(Week, levels = 1:52, ordered = TRUE),
         p1prom = as.logical(p1prom),
         p2prom = as.logical(p2prom))
weekly_store |> head(n=5)

# A tibble: 5 × 10
  storeNum Year  Week  p1sales p2sales p1price p2price p1prom p2prom country
  <fct>    <ord> <ord>   <dbl>   <dbl>   <dbl>   <dbl> <lgl>  <lgl>  <chr>  
1 101      1     1         127     106    2.29    2.29 FALSE  FALSE  US     
2 101      1     2         137     105    2.49    2.49 FALSE  FALSE  US     
3 101      1     3         156      97    2.99    2.99 TRUE   FALSE  US     
4 101      1     4         117     106    2.99    3.19 FALSE  FALSE  US     
5 101      1     5         138     100    2.49    2.59 FALSE  TRUE   US

Inspect data: the base R way

as.data.frame(weekly_store) |> str()

'data.frame':   2080 obs. of  10 variables:
 $ storeNum: Factor w/ 20 levels "101","102","103",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Year    : Ord.factor w/ 2 levels "1"<"2": 1 1 1 1 1 1 1 1 1 1 ...
 $ Week    : Ord.factor w/ 52 levels "1"<"2"<"3"<"4"<..: 1 2 3 4 5 6 7 8 9 10 ...
 $ p1sales : num  127 137 156 117 138 115 116 106 116 145 ...
 $ p2sales : num  106 105 97 106 100 127 90 126 94 91 ...
 $ p1price : num  2.29 2.49 2.99 2.99 2.49 2.79 2.99 2.99 2.29 2.49 ...
 $ p2price : num  2.29 2.49 2.99 3.19 2.59 2.49 3.19 2.29 2.29 2.99 ...
 $ p1prom  : logi  FALSE FALSE TRUE FALSE FALSE FALSE ...
 $ p2prom  : logi  FALSE FALSE FALSE FALSE TRUE FALSE ...
 $ country : chr  "US" "US" "US" "US" ...

Inspect data: the tidyverse way

weekly_store |> glimpse()

Rows: 2,080
Columns: 10
$ storeNum <fct> 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 1…
$ Year     <ord> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Week     <ord> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ p1sales  <dbl> 127, 137, 156, 117, 138, 115, 116, 106, 116, 145, 123, 169, 1…
$ p2sales  <dbl> 106, 105, 97, 106, 100, 127, 90, 126, 94, 91, 104, 73, 79, 10…
$ p1price  <dbl> 2.29, 2.49, 2.99, 2.99, 2.49, 2.79, 2.99, 2.99, 2.29, 2.49, 2…
$ p2price  <dbl> 2.29, 2.49, 2.99, 3.19, 2.59, 2.49, 3.19, 2.29, 2.29, 2.99, 2…
$ p1prom   <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ p2prom   <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,…
$ country  <chr> "US", "US", "US", "US", "US", "US", "US", "US", "US", "US", "…

Summarize data: the R base way

weekly_store |> summary()

    storeNum    Year          Week         p1sales       p2sales     
 101    : 104   1:1040   1      :  40   Min.   : 73   Min.   : 51.0  
 102    : 104   2:1040   2      :  40   1st Qu.:113   1st Qu.: 84.0  
 103    : 104            3      :  40   Median :129   Median : 96.0  
 104    : 104            4      :  40   Mean   :133   Mean   :100.2  
 105    : 104            5      :  40   3rd Qu.:150   3rd Qu.:113.0  
 106    : 104            6      :  40   Max.   :263   Max.   :225.0  
 (Other):1456            (Other):1840                                
    p1price         p2price       p1prom          p2prom       
 Min.   :2.190   Min.   :2.29   Mode :logical   Mode :logical  
 1st Qu.:2.290   1st Qu.:2.49   FALSE:1872      FALSE:1792     
 Median :2.490   Median :2.59   TRUE :208       TRUE :288      
 Mean   :2.544   Mean   :2.70                                  
 3rd Qu.:2.790   3rd Qu.:2.99                                  
 Max.   :2.990   Max.   :3.19                                  
                                                               
   country         
 Length:2080       
 Class :character  
 Mode  :character

Summarize data: the skimr way
- Ups the table is really big!!! Try it in your console to see the complete table

weekly_store |> skim()

Count data: the R base way

table(weekly_store$p1price)


2.19 2.29 2.49 2.79 2.99 
 395  444  423  443  375

Count data: the tidyverse way

weekly_store |> count(p1price)

# A tibble: 5 × 2
  p1price     n
    <dbl> <int>
1    2.19   395
2    2.29   444
3    2.49   423
4    2.79   443
5    2.99   375

Data visualization

Figure 1: Analogy of data visualization as painting step by step (Watercolor portrait - Step by Step by Hector Trunnec (Valencia, Spain) 2015-03-03)

Histograms: the base R way

weekly_store$p1sales |> hist()

Histograms: the base R way

weekly_store$p1sales |> hist(main = "Product 1 Weekly Sales Frequencies, All Stores",
                             xlab = "Product1 Sales (Units)",
                             ylab = "Count")

Histograms: the base R way

weekly_store$p1sales |> hist(main = "Product 1 Weekly Sales Frequencies, All Stores",
                             xlab = "Product1 Sales (Units)" ,
                             ylab = "Count", 
                             breaks = 30, 
                             col =  "lightblue")

Histograms: the base R way

weekly_store$p1sales |> hist(main = "Product 1 Weekly Sales Frequencies, All Stores",
                             xlab = "Product1 Sales (Units)" ,
                             ylab = "Frequency", 
                             breaks = 30, 
                             col =  "lightblue", 
                             freq = FALSE,
                             xaxt = "n")

Histograms: the base R way

weekly_store$p1sales |> hist(main = "Product 1 Weekly Sales Frequencies, All Stores",
                             xlab = "Product1 Sales (Units)" ,
                             ylab = "Frequency", 
                             breaks = 30, 
                             col =  "lightblue", 
                             freq = FALSE,
                             xaxt = "n")
axis(side=1 , at=seq(from = 60, to = 300, by = 20))

Histograms: the base R way

weekly_store$p1sales |> hist(main = "Product 1 Weekly Sales Frequencies, All Stores",
                             xlab = "Product1 Sales (Units)" ,
                             ylab = "Frequency", 
                             breaks = 30, 
                             col =  "lightblue", 
                             freq = FALSE,
                             xaxt = "n")
axis(side=1 , at=seq(from = 60, to = 300, by = 20))
lines(x = density(weekly_store$p1sales, bw=10), type="l", col="darkred", lwd=2)

Histograms: the tidyverse way

weekly_store |> ggplot()

Histograms: the tidyverse way

weekly_store |> ggplot() + 
  geom_histogram(aes(x = p1sales, y = after_stat(density)),
                 color = "black", fill = "lightblue", bins = 30)

Histograms: the tidyverse way

weekly_store |> ggplot() + 
  geom_histogram(aes(x = p1sales, y = after_stat(density)),
                 color = "black", fill = "lightblue", bins = 30) +
  geom_density(aes(x=p1sales),
               bw=10, color="darkred",
               linetype = "solid", linewidth = 1)

Histograms: the tidyverse way

weekly_store |> ggplot() + 
  geom_histogram(aes(x=p1sales, y = after_stat(density)),
                 color = "black", fill = "lightblue", bins = 30) +
  geom_density(aes(x=p1sales),
               bw=10, color="darkred", linetype="solid", linewidth=1) + 
  scale_x_continuous(breaks = seq(from = 60, to = 300, by = 20))

Histograms: the tidyverse way

weekly_store |> ggplot() + 
  geom_histogram(aes(x = p1sales, y = after_stat(density)),
                 color = "black", fill = "lightblue", bins = 30) +
  geom_density(aes(x = p1sales),
               bw = 10, color = "darkred", linetype = "solid", linewidth = 1) + 
  scale_x_continuous(breaks = seq(from = 60, to = 300, by = 20)) +
  labs(x = "Product1 Sales (Units)", y = "Frequency", 
       title = "Product 1 Weekly Sales Frequencies, All Stores")

Boxplots: the base R way
- Boxplot product 2 sales by promotion

boxplot(weekly_store$p2sales ~ weekly_store$p2prom,
        main = "Weekly sales of P2 with and without promotion",
        xlab = "Weekly unit sales", ylab = "P2 promoted in store?",
        horizontal = TRUE, las = 1)

Boxplots: the base R way
- Boxplot product 2 sales by promotion

boxplot(weekly_store$p2sales ~ weekly_store$p2prom,
        main = "Weekly sales of P2 with and without promotion",
        xlab = "Weekly unit sales", ylab = "P2 promoted in store?",
        horizontal = TRUE, las = 1, yaxt = "n")
axis(side = 2, at = c(1,2), labels = c("No", "Yes"))

Boxplots: the tidyverse way
- Boxplot product 2 sales by promotion

weekly_store |> ggplot()

Boxplots: the tidyverse way
- Boxplot product 2 sales by promotion

weekly_store |> ggplot() + 
  geom_boxplot(aes(x = p2sales, y = p2prom))

Boxplots: the tidyverse way
- Boxplot product 2 sales by promotion

weekly_store |> ggplot() + 
  geom_boxplot(aes(x = p2sales, y = p2prom)) + 
  scale_y_discrete(labels = c("No", "Yes"))

Boxplots: the tidyverse way
- Boxplot product 2 sales by promotion

weekly_store |> ggplot() + 
  geom_boxplot(aes(x = p2sales, y = p2prom)) + 
  scale_y_discrete(labels = c("No", "Yes")) +
  labs(x = "Weekly unit sales", y = "P2 promoted in store?", 
       title = "Weekly sales of P2 with and without promotion")

Questions and answers with data

In what countries the company sell more units of product 2?
- Preparing the data

weekly_store_sales_by_country <- weekly_store |> 
  group_by(country)
weekly_store_sales_by_country

# A tibble: 2,080 × 10
# Groups:   country [7]
   storeNum Year  Week  p1sales p2sales p1price p2price p1prom p2prom country
   <fct>    <ord> <ord>   <dbl>   <dbl>   <dbl>   <dbl> <lgl>  <lgl>  <chr>  
 1 101      1     1         127     106    2.29    2.29 FALSE  FALSE  US     
 2 101      1     2         137     105    2.49    2.49 FALSE  FALSE  US     
 3 101      1     3         156      97    2.99    2.99 TRUE   FALSE  US     
 4 101      1     4         117     106    2.99    3.19 FALSE  FALSE  US     
 5 101      1     5         138     100    2.49    2.59 FALSE  TRUE   US     
 6 101      1     6         115     127    2.79    2.49 FALSE  FALSE  US     
 7 101      1     7         116      90    2.99    3.19 FALSE  FALSE  US     
 8 101      1     8         106     126    2.99    2.29 FALSE  FALSE  US     
 9 101      1     9         116      94    2.29    2.29 FALSE  FALSE  US     
10 101      1     10        145      91    2.49    2.99 FALSE  FALSE  US     
# ℹ 2,070 more rows

In what countries the company sell more units of product 2?
- Preparing the data

weekly_store_sales_by_country <- weekly_store |> 
  group_by(country) |>
  summarise(sum_p2sales = sum(p2sales))
weekly_store_sales_by_country

# A tibble: 7 × 2
  country sum_p2sales
  <chr>         <dbl>
1 AU             9934
2 BR            21362
3 CN            20911
4 DE            52263
5 GB            31264
6 JP            41344
7 US            31248

In what countries the company sell more units of product 2?
- Preparing the data

weekly_store_sales_by_country <- weekly_store |> 
  group_by(country) |>
  summarise(sum_p2sales = sum(p2sales)) |>
  mutate(country = fct_reorder(.f = country, .x = sum_p2sales))
weekly_store_sales_by_country

# A tibble: 7 × 2
  country sum_p2sales
  <fct>         <dbl>
1 AU             9934
2 BR            21362
3 CN            20911
4 DE            52263
5 GB            31264
6 JP            41344
7 US            31248

In what countries the company sell more units of product 2?
- Visualizing data

weekly_store_sales_by_country |> ggplot()

In what countries the company sell more units of product 2?
- Visualizing data

weekly_store_sales_by_country |> ggplot() + 
  geom_col(aes(x = country, y = sum_p2sales))

In what countries the company sell more units of product 2?
- Visualizing data

weekly_store_sales_by_country |> ggplot() + 
  geom_col(aes(x = country, y = sum_p2sales, fill = country),
           color = "black", show.legend = FALSE)

In what countries the company sell more units of product 2?
- Visualizing data

weekly_store_sales_by_country |> ggplot() + 
  geom_col(aes(x = country, y = sum_p2sales, fill = country),
           color = "black", show.legend = FALSE) +
  scale_x_discrete(labels = c("Australia", "Japan", "Brazil", 
                              "Britain", "Germany", "China", "United States"))

In what countries the company sell more units of product 2?
- Visualizing data

weekly_store_sales_by_country |> ggplot() + 
  geom_col(aes(x = country, y = sum_p2sales, fill = country),
           color = "black", show.legend = FALSE) +
  scale_x_discrete(labels = c("Australia", "Japan", "Brazil", 
                              "Britain", "Germany", "China", "United States")) +
  labs(x = NULL, y = "Sales (Units)",
       title = "Sales in units per country")

Acknowledgments

To my family that supports me
To the taxpayers of Colombia and the UMNG students who pay my salary
To the Business Science and R4DS Online Learning communities where I learn R and \(\pi\)-thon
To the R Core Team, the creators of RStudio IDE, Quarto and the authors and maintainers of the packages tidyverse, skimr and tinytex for allowing me to access these tools without paying for a license

References

Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics (2nd ed. 2019). Springer International Publishing : Imprint: Springer. https://doi-org.ezproxy.umng.edu.co/10.1007/978-3-030-14316-9